Change order of imports and variable setting.

This may help get things into the right place under repo2docker.

binder/requirements.txt

@@ -1,7 +1,8 @@
 repo2data>=2.6.0
 jupyter-book==0.14.0
 afqinsight==0.6.1
-pyAFQ==1.3.5
+pyAFQ==1.3.6
 ray
 plotly==5.3.0
-trx-python==0.3
+trx-python==0.3
+jupyter-server<2.0.0

content/01-pyafq.ipynb

@@ -28,7 +28,7 @@
    "source": [
     "## Setup \n",
     "\n",
-    "We start with a few setup steps, we start by importing the functionality that \n",
+    "We start with a few setup steps. We start by importing the functionality that \n",
     "we will use from pyAFQ"
    ]
   },
@@ -38,36 +38,48 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from AFQ.api.group import GroupAFQ"
+    "import os\n",
+    "import os.path as op\n",
+    "\n",
+    "pwd = op.dirname(os.getcwd())\n",
+    "\n",
+    "os.environ[\"TEMPLATEFLOW_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
+    "os.environ[\"DIPY_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
+    "os.environ[\"AFQ_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import os.path as op\n",
-    "\n",
-    "pwd = op.dirname(os.getcwd())\n",
-    "\n",
-    "os.environ[\"TEMPLATEFLOW_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
-    "os.environ[\"DIPY_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
-    "os.environ[\"AFQ_HOME\"] = op.join(pwd, \"..\", \"data_\", \"tractometry\")\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import nibabel as nib\n",
     "import plotly\n",
     "import pandas as pd\n",
-    "\n"
+    "from AFQ.api.group import GroupAFQ"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Set tractography parameters (optional)\n",
+    "## Set up tracking parameters \n",
+    "\n",
+    "\n",
     "We make create a `tracking_params` variable, which we will pass to the\n",
     "GroupAFQ object which specifies that we want 25,000 seeds randomly\n",
     "distributed in the white matter. We only do this to make this example \n",

paper.md

@@ -28,14 +28,12 @@ bibliography: paper.bib
 
 Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess
 the physical properties of long-range brain connections [@Yeatman2012AFQ].
-Here, we present an integrative ecosystem of software that performs all steps
+We present an integrative ecosystem of software that performs all steps
 of tractometry: post-processing of dMRI data, delineation of major white matter
 pathways, and modeling of the tissue properties within them. This ecosystem
 also provides tools that extract insights from these measurements, including
 novel implementations of machine learning and statistical analysis methods that
-consider the unique structure of tractometry data [@RichieHalford2021SGL,
-@Muncy2022GAMs], as well as tools for visualization and interpretation of the
-results [@Yeatman2018AFQBrowser, @Kruper2024-ke]. Taken together, these
+consider the unique structure of tractometry data [@RichieHalford2021SGL,@Muncy2022GAMs], as well as tools for visualization and interpretation of the results [@Yeatman2018AFQBrowser,@Kruper2024-ke]. Taken together, these
 open-source software tools provide a comprehensive environment for the analysis
 of dMRI data.